Fuzzy-ART in network anomaly detection with feature-reduction dataset
Conference proceedings article
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Author list: Ngamwitthayanon N., Wattanapongsakorn N.
Publisher: Hindawi
Publication year: 2011
Start page: 116
End page: 121
Number of pages: 6
ISBN: 9788988678428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
The validation of Fuzzy-Adaptive Resonance Theory (Fuzzy-ART or F-ART) was made in our work on Network Anomaly Intrusion Detection (NAID) application. Feature reduction of KDD 99 dataset was applied to the F-ART model and produced superior performance. We found the effectiveness of FART on clustering data instances into normal and anomalous traffic. The detection performance was clearly improved compare to the detection with the full-feature dataset. The results validated the capability of F-ART with one shot fast learning on the effectiveness of this adaptive learning algorithm along with the robustness and fast response that can provide a real-time network anomaly detection. ฉ 2011 AICIT.
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